Pemilihan kerjaya di kalangan pelajar aliran perdagangan sekolah menengah teknik : satu kajian kes

This research is a survey to determine the career chosen of form four student in commerce streams. The important aspect of the career chosen has been divided into three, first is information about career, type of career and factor that most influence students in choosing a career. The study was conducted at Sekolah Menengah Teknik Kajang, Selangor Darul Ehsan. Thirty six form four students was chosen by using non-random sampling purpose method as respondent. All information was gather by using questionnaire. Data collected has been analyzed in form of frequency, percentage and mean. Results are performed in table and graph. The finding show that information about career have been improved in students career chosen and mass media is the main factor influencing students in choosing their career

Designing for Ballet Classes: Identifying and Mitigating Communication Challenges Between Dancers and Teachers

Dancer-teacher communication in a ballet class can be challenging: ballet is one of the most complex forms of movements, and learning happens through multi-faceted interactions with studio tools (mirror, barre, and floor) and the teacher. We conducted an interview-based qualitative study with seven ballet teachers and six dancers followed by an open-coded analysis to explore the communication challenges that arise while teaching and learning in the ballet studio. We identified key communication issues, including adapting to multi-level dancer expertise, transmitting and realigning development goals, providing personalized corrections and feedback, maintaining the state of flow, and communicating how to properly use tools in the environment. We discuss design implications for crafting technological interventions aimed at mitigating these communication challenges

Processing of Electronic Health Records using Deep Learning: A review

Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data generated from health and wellbeing devices (such as personal trackers), a significant contribution to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms

SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine

Traditional medicine typically applies one-size-fits-all treatment for the entire patient population whereas precision medicine develops tailored treatment schemes for different patient subgroups. The fact that some factors may be more significant for a specific patient subgroup motivates clinicians and medical researchers to develop new approaches to subgroup detection and analysis, which is an effective strategy to personalize treatment. In this study, we propose a novel patient subgroup detection method, called Supervised Biclustring (SUBIC) using convex optimization and apply our approach to detect patient subgroups and prioritize risk factors for hypertension (HTN) in a vulnerable demographic subgroup (African-American). Our approach not only finds patient subgroups with guidance of a clinically relevant target variable but also identifies and prioritizes risk factors by pursuing sparsity of the input variables and encouraging similarity among the input variables and between the input and target variable

Technology and Caregiving: Emerging Interventions and Directions for Research.

An array of technology-based interventions has increasingly become available to support family caregivers, primarily focusing on health and well-being, social isolation, financial, and psychological support. More recently the emergence of new technologies such as mobile and cloud, robotics, connected sensors, virtual/augmented/mixed reality, voice, and the evermore ubiquitous tools supported by advanced data analytics, coupled with the integration of multiple technologies through platform solutions, have opened a new era of technology-enabled interventions that can empower and support family caregivers. This paper proposes a conceptual framework for identifying and addressing the challenges that may need to be overcome to effectively apply technology-enabled solutions for family caregivers. The paper identifies a number of challenges that either moderate or mediate the full use of technologies for the benefit of caregivers. The challenges include issues related to equity, inclusion, and access; ethical concerns related to privacy and security; political and regulatory factors affecting interoperability and lack of standards; inclusive/human-centric design and issues; and inherent economic and distribution channel difficulties. The paper concludes with a summary of research questions and issues that form a framework for global research priorities

Pain Level Detection From Facial Image Captured by Smartphone

Accurate symptom of cancer patient in regular basis is highly concern to the medical service provider for clinical decision making such as adjustment of medication. Since patients have limitations to provide self-reported symptoms, we have investigated how mobile phone application can play the vital role to help the patients in this case. We have used facial images captured by smart phone to detect pain level accurately. In this pain detection process, existing algorithms and infrastructure are used for cancer patients to make cost low and user-friendly. The pain management solution is the first mobile-based study as far as we found today. The proposed algorithm has been used to classify faces, which is represented as a weighted combination of Eigenfaces. Here, angular distance, and support vector machines (SVMs) are used for the classification system. In this study, longitudinal data was collected for six months in Bangladesh. Again, cross-sectional pain images were collected from three different countries: Bangladesh, Nepal and the United States. In this study, we found that personalized model for pain assessment performs better for automatic pain assessment. We also got that the training set should contain varying levels of pain in each group: low, medium and high

Touch Screen Avatar English Learning System For University Students Learning Simplicity

This paper discusses on touch screen avatar for an English language learning application system. The system would be a combination of avatar as Animated Pedagogical Agent (APA) and a touch screen application that adapt the up to date gesture-based computing which is found as having potential to change the way how we learn as it could reduce the amount of Information Communication Technology (ICT) devices used during teaching and learning process. The key here is interaction between university students and touch screen avatar intelligent application system as well as learning resources that could be learned anytime anywhere twenty four hours in seven days 24/7 based on their study time preference where they could learn at their own comfort out of the tradition. The students would be provided with a learning tool that could help them learn interactively with the current trend which they might be interested with based on their own personalization. Apart from that, their performance shall be monitored from a distance and evaluated to avoid disturbing their learning process from working smoothly and getting rid of feeling of being controlled. Thus, the students are expected to have lower affective filter level that may enhance the way they learn unconsciously. Keywords: Gesture-Based Computing, Avatar, Portable Learning Tool, Interactivity, Language Learnin

Designing an e-tutoring system for large classes: mixed-method research

This study aimed at assessing the perceptions of 167 teachers about the tutoring system adopted in an online training course involving teachers from 20 Schools of Sesimbra, Setúbal and Palmela counties. The course, called “Distributed Knowledge with Web 2.0”, was officially certified as a blended learning modality, with the duration of 50 hours, 41 of which occurred online in two editions, the first in February and the second in July of 2012, each one of them involving respectively 82 and 85 teachers, divided in four classes with about 20 trainees each. This blended learning course was designed at producing educational materials in digital format, and included autonomous and group activities, knowledge sharing and reflection. A learning environment, supported by the Ning platform, was set up. At the end of the course, the trainees answered to a pencil and paper survey, in order to evaluate the adopted online tutoring strategy. Additionally the trainees’ final reports contained evidence of how the trainees assessed the tutoring model component of the course; both the survey and the reports were the basis for this research. The results show that the teachers who attended the two course editions disclosed very positive perceptions about online learning, a modality they consider adequate to their current professional status and conditions. The trainees also showed their intention of, in the future, opting for blended training arrangements. Future developments of this study involve a content analysis of the tutor’s posts, in order to understand more accurately the tutor’s messages characteristics, in their social and cognitive dimensions

An open learning environment for the diagnosis, assistance and evaluation of students based on artificial intelligence

The personalized diagnosis, assistance and evaluation of students in open learning environments can be a challenging task, especially in cases that the processes need to be taking place in real-time, classroom conditions. This paper describes the design of an open learning environment under development, designed to monitor the comprehension of students, assess their prior knowledge, build individual learner profiles, provide personalized assistance and, finally, evaluate their performance by using artificial intelligence. A trial test has been performed, with the participation of 20 students, which displayed promising results